Testing
Devin AI is the most autonomous coding assistant on the market. It doesn't just suggest code — it executes tasks autonomously. Give it a GitHub issue, it reads the codebase, writes a fix, runs tests, and submits a pull request. The PR comes with test results attached. For QA
Test Automation
A growing share of traffic hitting business websites isn't a person clicking around — it's an AI agent filling out a form, submitting a product to a directory, or calling an API on a user's behalf. These agents follow the same UI and API contracts
Testing
Shipping LLM features without automated evaluation is like deploying code without running tests. DeepEval and Promptfoo are the two leading open-source frameworks for automating LLM evaluation — they let you define quality metrics, run them against your prompts, and fail CI when quality drops. This guide covers both tools: what
Testing
Reward models in RLHF pipelines turn human preferences into scalar scores. Evaluate them with preference accuracy, Kendall tau ranking correlation, and human-AI agreement rate. A reward model with 65% pairwise accuracy can still produce a misaligned policy — measure all three dimensions before trusting it. Reinforcement Learning from Human Feedback
Testing
Cursor has changed how fast developers can ship features. A task that used to take hours can take minutes. That speed is real and it's valuable. But it creates a new problem: your QA process was built for human-speed development, and now it's the bottleneck.
Testing
Retrieval-Augmented Generation (RAG) pipelines are complex systems with multiple failure points: chunking can destroy context, retrieval can miss relevant documents, and the LLM can hallucinate facts not present in the retrieved chunks. Testing a RAG system requires evaluating each stage and the full pipeline together. This guide covers how
Testing
Claude has become one of the most effective tools for writing automated tests. Not because it's faster than other AI models — it's fast, but speed isn't the differentiator. Claude is effective for testing because it reasons about what should be tested before writing a
Testing
You can't ship LLM-based features without testing them. Unlike traditional software where the same input always produces the same output, LLMs are probabilistic — the same prompt can return different results across runs, models, and versions. Without structured prompt testing, you're deploying blind. This guide covers
Testing
Prompt testing is the practice of verifying that your AI product's prompts produce the intended behavior. It's harder than it sounds. Prompts are non-deterministic inputs to non-deterministic models. Testing them requires a different mindset than testing code. This guide covers practical prompt testing strategies
Testing
AI coding assistants have fundamentally changed how developers write software.
Testing
Single-turn LLM evaluation is well understood: give the model a prompt, compare its output to a reference, compute a score.
Testing
Reasoning models — OpenAI's o3/o4-mini, Anthropic's Claude with extended thinking, and DeepSeek R1 — require different evaluation strategies than standard language models. They trade token budget for reasoning quality, produce verifiable intermediate steps, and have different failure modes. This guide covers building eval frameworks specifically for